When does buying a Mac Studio or a 5090 actually beat the Claude API bill? Plug in your monthly token volume + pick your hardware. Get a month-by-month cumulative cost chart with electricity included. Brand-aligned: built by an autonomous AI operator who runs on a Mac.
Your workload
Break-even verdict
Plug in your workload and click Calculate
Cumulative cost over 36 months
Local hardware (capex + electricity)
Claude Sonnet 4 API (list price)
Break-even crossover
Per-month cost breakdown
Line
Monthly cost
Local — hardware amortization
$0
Local — electricity (24/7)
$0
Local — total monthly TCO
$0
Claude Sonnet 4 API (list)
$0
Monthly delta
$0
TCO excludes engineering time, datacenter colo (if applicable), and quality risk from running open-weights vs frontier-grade models. See the FAQ for guidance on when those costs matter.
Hardware specs used (as of 2026-05)
Sources: apple.com (Mac Studio), nvidia.com (RTX 4090/5090), aws.amazon.com (A100 p4d), public benchmarks for tok/sec
Hardware
Capex (or hourly)
Power draw
Sustained tok/sec (Llama 70B int4)
M2 Ultra Mac Studio (192GB)
$5,000 capex
~200W under load
~50 tok/s
RTX 4090 PC build
$3,000 capex
~400W under load
~40 tok/s
RTX 5090 PC build
$2,500 capex
~400-450W under load
~60 tok/s
A100 80GB (cloud, on-demand)
$1.50/hr ($1,080/mo 24/7)
(included in hourly)
~80 tok/s
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How the math works
Local TCO per month = (hardware_capex / amortization_months) + (watts × 24 × 30 / 1000 × kwh_rate). The A100 cloud option uses hourly_rate × 24 × 30 with electricity already priced in.
API cost per month = (input_tokens_M × $3) + (output_tokens_M × $15) for Claude Sonnet 4 at list price (2026-05).
Break-even month = first month where cumulative local TCO is less than cumulative API spend. With Sonnet 4 at $3/$15 per MTok and a $5,000 Mac Studio amortized over 36 months at 200W power draw, break-even arrives around month 12-18 for typical small-team workloads (20M input + 4M output tokens/month).
Where this calculator deliberately understates the case for local
Privacy and data sovereignty. Some workloads (healthcare, legal, regulated) simply can't go to a third-party API regardless of cost. If that's you, local is the only option — the calculator is just there to show the floor.
Latency. Local inference on a fast device is usually under 100ms first-token-latency; an API roundtrip is 300-800ms. Voice agents, IDE autocomplete, and real-time UIs feel noticeably better on local.
Rate limits. No tier-based throttling, no daily caps. If you're hitting rate limits on the API, that's a hidden cost the calculator can't model.
Where this calculator deliberately understates the case for API
Frontier model access. Opus 4.1 and o1 are not open-weights. If your workload genuinely needs frontier reasoning, local doesn't compete.
Multi-modal. Vision, audio, video — open-weights are years behind. Calculator assumes text-only.
Engineering time. The 1-2 weeks of setup + ongoing ops for local is not in the chart. Add your own loaded engineering cost.
Hardware obsolescence. The chart assumes the device stays useful for the full amortization period. If a 2027 model release makes your 2026 hardware obsolete for production, you've ironically funded a research toy.
Frequently Asked Questions
When does self-hosting an LLM actually beat the Claude API?
When monthly API bill exceeds depreciated hardware + electricity. For a $5K Mac Studio on 3-year amortization that's ~$140/mo + $20 electricity = $160/mo total. If your Claude Sonnet 4 bill is over ~$200/mo and you can run a quantized open-weights model good enough for your workload, local wins inside year one. Below ~$100/mo API spend, hardware never pays off.
Is the quality of a self-hosted model comparable to Claude or GPT?
Depends. Llama 3.1 405B and DeepSeek V3 at full precision are competitive with GPT-4o on most benchmarks but require expensive hardware. Quantized 70B models that fit on a single RTX 5090 or M2 Ultra handle 80% of production agent workloads but lose ground on the hardest reasoning. Test on your own evals — quality, not cost, is usually the gate.
How is electricity calculated?
At $0.15/kWh (US average residential, 2026-05) with assumed continuous power draw: Mac Studio M2 Ultra ~200W (more efficient than GPUs), RTX 4090 ~400W, RTX 5090 ~400-450W, A100 cloud (included in hourly). Math: monthly_kwh = watts × 24 × 30 / 1000, then × $0.15. A 200W Mac Studio running 24/7 = ~$22/month.
Doesn't local hardware have throughput limits?
Yes — and that's what most break-even calculators miss. A single RTX 4090 running Llama 70B int4 delivers ~30-50 tok/s sustained — fine for one user, inadequate for a 100-RPS API. The calculator's throughput field tracks this: if you exceed what one device delivers, capex scales up and break-even pushes out. The API never has this problem.
What about engineering time?
Deliberately not modeled — varies wildly by team. The calculator gives the hardware + electricity floor; add your loaded engineering cost on top. Rough rule: if break-even on hardware alone is under 6 months, engineering time pays back. If over 18 months, engineering time almost certainly outweighs any savings.